iProcess: Enabling IoT Platforms in Data-Driven Knowledge-Intensive Processes

Beheshti, Amin; Schiliro, Francesco; Ghodratnama, Samira; Amouzgar, Farhad; Benatallah, Boualem; Yang, Jian; Sheng, Quan Z.; Casati, Fabio; Motahari-Nezhad, Hamid Reza

doi:10.1007/978-3-319-98651-7_7

Cited by 32 publications

(15 citation statements)

References 21 publications

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“…New technologies such as the Internet of Things are aligning innovation to develop internal capacity for knowledge management [30], which helps organizations transfer knowledge from one sector to another and also helps monitor significant procedures that support the decision-making process [31]. Nevertheless, this advanced technology requires a new approach to management, often requiring the ongoing intervention of special skills possessed by knowledge workers to steer the workflow system [32].…”

Section: Knowledge Worker Productivity Theorymentioning

confidence: 99%

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

Mote

Karadaş

2022

Sustainability

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While there are existing empirical reviews regarding the relation between knowledge workers (KW) and knowledge transfer (KT), no study has examined the association of automation and knowledge transfer. The study describes knowledge workers as employees embedded with special abilities and having the main role of transfering their abilities to other workers in the organization. Additionally, automation (AUT) is described in the study as a technological process embedded with tacit knowledge. Therefore, using knowledge worker productivity theory and the technology acceptance model, this study aims to recommend and test a research model which examines the mediating role of knowledge transfer between automation, knowledge workers, and employee outcomes (employee creativity (EC) and innovative performance (IP)). The study evaluates the significant influence of knowledge transfer on innovative performance and creativity of other employees within the organization. This study applied a judgmental non-probability sampling research strategy to gather data from employees at an industrial firm in Japan by administering a questionnaire via Google Form. The data were analyzed with partial least squares based on structural equation modeling aimed at testing the predictive power and relationships of the model estimates. The results found support for the mediating role of knowledge transfer between automation and employee creativity as well as between automation and innovative performance. However, the study found partial support that knowledge transfer mediates the relationship between knowledge worker and employee creativity, as well as knowledge worker and innovative performance. In the same, automation and knowledge workers have significant impacts on knowledge transfer. Knowledge transfer has a significant impact on employee creativity and innovative performance.

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Section: Knowledge Worker Productivity Theorymentioning

confidence: 99%

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

Mote

Karadaş

2022

Sustainability

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“…An important application in this category is to understand customer's personality, behaviour and attitude in business processes [151], [152] and to predict how their trust in companies and products may change over time. Business processes are a set of tasks and activities performed to accomplish a specific organisational goal [153], [154]. For example, consider a bank customer who has decided to change their bank or a specific product offered by a bank.…”

Section: Approach Time Complexitymentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

Self Cite

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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“…Processes can be structured or adhoc. Adhoc processes are quite data-driven [1][54] and knowledge intensive [10][74] [71]. Customer segmentation is an example of an adhoc process and can facilitate Personalization, Effective Acquisition and Retention, and intelligent marketing.…”

Section: Motivating Scenariomentioning

confidence: 99%

Intelligent Vector-based Customer Segmentation in the Banking Industry

Mousaeirad

2020

Preprint

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Customer Segmentation is the process of dividing customers into groups based on common characteristics. An intelligent Customer Segmentation will not only enable an organization to effectively allocate marketing resources (e.g., Recommender Systems in the Banking sector) but also it will enable identifying the customer cohorts that are most likely to benefit from a specific policy (e.g., to discover diverse patient groups in the Health sector). While there has been a significant improvement in approaches to Customer Segmentation, the main challenge remains to be the understanding of the reasons behind the segmentation need. This task is challenging as it is subjective and depends on the goal of segmentation as well as the analyst's perspective. To address this challenge, in this paper, we present an intelligent vector-based customer segmentation approach. The proposed approach will leverage feature engineering to enable analysts to identify important features (from a pool of features such as demographics, geography, psychographics, behavioral, and more) and feed them into a neural embedding framework named Cus-tomer2Vec. The Customer2Vec combines the neural network classification and clustering methods as supervised and unsupervised learning techniques to embed the customer vector. We adopt a typical scenario in the Banking Sector to highlight how Customer2Vec significantly improves the quality of the segmentation and detecting customer similarities.

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iProcess: Enabling IoT Platforms in Data-Driven Knowledge-Intensive Processes

Cited by 32 publications

References 21 publications

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

A Survey on Trust Prediction in Online Social Networks

Intelligent Vector-based Customer Segmentation in the Banking Industry

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